*These files and folders are available to reproduce the findings in Ji, Keiser, and Kling (Temporal Reliability of Welfare Estimates from 
Revealed Preferences). Questions on code should be directed to Yongjie Ji (yongjiej@iastate.edu).

*We are not able to provide two of the main datasets for this project. These data have been collected over a decade as part of the Iowa Lakes 
Valuation Project (http://www.card.iastate.edu/lakes/) and have involved many parties. While we have permission to use the data for this project, 
we do not have permission to make these data publicly available. Other researchers interested in using the data for replication purposes can obtain
these data by contacting Dr. Catherine Kling (ckling@cornell.edu) for the recreation data and Dr. John Downing (downing@d.umn.edu) for the water 
quality data.

*We have established these folders and code such that if one obtains the data above, one should be able to replicate our analysis. We also include folders
that include "demo" datasets for researchers to see how the code works. 

*Lastly, we note that for replication, a user must have Stata and a GPU enabled machine with Matlab.


The description of each folder

\MATLAB\Orginal_Code
	This code will replicate the estimation commands in the paper if the original datasets are obtained. 


*The following folders are provided to give the reader a better understanding of this code using sample datasets. 

\MATLAB\RUM 
RUM
	It includes a set of core MATLAB scripts and demo data sets. We wrote scripts to perform the majority of RUM specifications both in CPU and GPU versions. However, we found that the GPU enabled scripts run much fast than the CPU version of the specification. In the example.m, the computational savings are more than 90%. As such, these GPU version scripts are used in the bootstrap analysis. We put some comments in each script to help understand each steps. 
	
KT	
    It includes the necessary parts of scripts writen by Prof. Klaiber, H Allen (https://aede.osu.edu/programs/h-allen-klaiber-0/home) for Classical estimation of Kuhn-Tucker model. 
	
	It also includes the necessary demo data set to estimate the KT model, although we should mention that these data sets are not the original data set we used in the paper (for confidentiality reasons). 
	
Swait	
    It includes scripts to perform the testing method proposed in Swait and Louviere (1993). The difference is that in the original paper, the authors proposed a grid search method to find out the optimal scale ratio (\mu in the paper). In our approach, we estimate the optional scale ratio along with other preference parameters thanks to computational improvements. 
	
Habit 
    It includes the scripts and demo data set to perform a habit-formation model proposed in Yi (2014). 	 
	
\STATA\Data
    It includes the data sets used in rt_main.do and rt_appendix.do 
	
\STATA\Figures 
    It includes generated figures in rt_main.do/rt_appendix.do. 
\STATA\Tables
    It includes generated tables in rt_main.do/rt_appendix.do.
\STATA\rt_main.do
    It reproduce tables and figures in the main manuscript. 
\STATA\rt_appendix.do 
    It reproduce tables and figures in the appendix. 



	
	
	
